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Allow user to change optimizer and objective function. Also added r-squared evaluator. Changed temp file location to user temp folder. Added a prediction dictionary to access the values predicted by model.

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titusquah
2020-06-05 14:20:59 -06:00
parent bc568a485b
commit a8fd716937
6 changed files with 361 additions and 81 deletions
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5
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@@ -2,16 +2,21 @@
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<w>conc</w>
<w>diluant</w> <w>diluant</w>
<w>disp</w>
<w>dodecane</w> <w>dodecane</w>
<w>equilibrate</w> <w>equilibrate</w>
<w>extractant</w> <w>extractant</w>
<w>ftol</w>
<w>kmol</w> <w>kmol</w>
<w>lmse</w> <w>lmse</w>
<w>maxiter</w>
<w>ndarray</w> <w>ndarray</w>
<w>pred</w> <w>pred</w>
<w>reeps</w> <w>reeps</w>
<w>scipy</w> <w>scipy</w>
<w>slsqp</w>
<w>thermo</w> <w>thermo</w>
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@@ -56,7 +56,7 @@
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27
README.md
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@@ -1,3 +1,26 @@
# parameter-estimation # REEPS
REEPS is a toolkit for estimating standard thermodynamic parameters for Gibbs minimization.
Extend a methodology for estimating standard thermodynamic parameters for Gibbs minimization in multiphase, multicomponent separations systems Extend a methodology for estimating standard thermodynamic parameters for Gibbs minimization in multiphase, multicomponent separations systems
## Installation
To install REEPS, clone the repository with the command
```
$ git clone https://xgitlab.cels.anl.gov/summer-2020/parameter-estimation.git
```
Navigate into the parameter-estimation folder with
```
cd parameter-estimation
```
and run the command below in your terminal
```
$ pip install -e.
```
### Dependencies
REEPS uses packages: cantera (https://cantera.org/), pandas, numpy, scipy, xml, seaborn, and matplotlib
## Usage
Do random stuff and pray.

2
data/xmls/twophase.xml
View File

@@ -50,7 +50,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 11:29 6-3-2020">-4704703.645715787</h0> <h0 units="J/mol" updated="Updated at 14:15 6-5-2020">-4704703.645715787</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>

273
reeps.py
View File

@@ -9,6 +9,8 @@ import seaborn as sns
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import shutil import shutil
import copy import copy
from inspect import signature
import os
sns.set() sns.set()
@@ -16,6 +18,7 @@ class REEPS:
"""REEPS (Rare earth extraction parameter searcher) """REEPS (Rare earth extraction parameter searcher)
Takes in experimental data Takes in experimental data
Returns parameters for GEM Returns parameters for GEM
Only good for 1 rare earth and 1 extractant
:param exp_csv_filename: (str) csv file name with experimental data :param exp_csv_filename: (str) csv file name with experimental data
:param phases_xml_filename: (str) xml file with parameters for equilibrium calc :param phases_xml_filename: (str) xml file with parameters for equilibrium calc
:param opt_dict: (dict) optimize info {species:{thermo_prop:guess} :param opt_dict: (dict) optimize info {species:{thermo_prop:guess}
@@ -27,8 +30,34 @@ class REEPS:
:param rare_earth_ion_name: (str) name of rare earth ion in xml file :param rare_earth_ion_name: (str) name of rare earth ion in xml file
:param aq_solvent_rho: (float) density of solvent (g/L) :param aq_solvent_rho: (float) density of solvent (g/L)
:param extractant_rho: (float) density of extractant (g/L) :param extractant_rho: (float) density of extractant (g/L)
:param diluant_rho: (float) density of extractant (g/L) :param diluant_rho: (float) density of diluant (g/L)
If no density is given, molar volume/molecular weight is used from xml If no density is given, molar volume/molecular weight is used from xml
:param objective_function: (function) function to compute objective
By default, the objective function is log mean squared error
of distribution ratio np.log10(re_org/re_aq)
Function needs to take inputs:
objective_function(predicted_dict, measured_df, **kwargs)
**kwargs is optional
Below is the guide for referencing predicted values
| To access | Use |
|------------------------------------- |--------------------------|
| predicted rare earth eq conc in aq | predicted_dict['re_aq'] |
| predicted rare earth eq conc in org | predicted_dict['re_org'] |
| predicted hydrogen ion conc in aq | predicted_dict['h'] |
| predicted extractant conc in org | predicted_dict['z'] |
| predicted rare earth distribution ratio | predicted_dict['re_d'] |
For measured values, use the column names in the experimental data file
:param optimizer: (function) function to perform optimization
By default, the optimizer is scipy's optimize function with
default_kwargs= {"method": 'SLSQP',
"bounds": [(1e-1, 1e1)*len(x_guess)],
"constraints": (),
"options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}}
Function needs to take inputs:
optimizer(objective_function, x_guess, **kwargs)
**kwargs is optional
:param temp_xml_file_path: (str) path to temporary xml file
default is local temp folder
""" """
def __init__(self, def __init__(self,
@@ -44,7 +73,12 @@ class REEPS:
aq_solvent_rho=None, aq_solvent_rho=None,
extractant_rho=None, extractant_rho=None,
diluant_rho=None, diluant_rho=None,
objective_function='Log-MSE',
optimizer='SLSQP',
temp_xml_file_path=None
): ):
self._built_in_obj_list = ['Log-MSE']
self._built_in_opt_list = ['SLSQP']
self._exp_csv_filename = exp_csv_filename self._exp_csv_filename = exp_csv_filename
self._phases_xml_filename = phases_xml_filename self._phases_xml_filename = phases_xml_filename
self._opt_dict = opt_dict self._opt_dict = opt_dict
@@ -57,9 +91,14 @@ class REEPS:
self._aq_solvent_rho = aq_solvent_rho self._aq_solvent_rho = aq_solvent_rho
self._extractant_rho = extractant_rho self._extractant_rho = extractant_rho
self._diluant_rho = diluant_rho self._diluant_rho = diluant_rho
self._objective_function = None
self._temp_xml_filename = "temp.xml" self.set_objective_function(objective_function)
shutil.copyfile(phases_xml_filename, self._temp_xml_filename) self._optimizer = None
self.set_optimizer(optimizer)
if temp_xml_file_path is None:
temp_xml_file_path = '{0}\\temp.xml'.format(os.getenv('TEMP'))
self._temp_xml_file_path = temp_xml_file_path
shutil.copyfile(phases_xml_filename, self._temp_xml_file_path)
self._phases = ct.import_phases(phases_xml_filename, phase_names) self._phases = ct.import_phases(phases_xml_filename, phase_names)
self._exp_df = pd.read_csv(self._exp_csv_filename) self._exp_df = pd.read_csv(self._exp_csv_filename)
@@ -69,6 +108,27 @@ class REEPS:
self._org_ind = None self._org_ind = None
self.set_in_moles(feed_vol=1) self.set_in_moles(feed_vol=1)
self._predicted_dict = None
self.update_predicted_dict()
@staticmethod
def log_mean_squared_error(predicted_dict, meas_df):
meas = meas_df['D(m)'].values
pred = predicted_dict['re_org'] / predicted_dict['re_aq']
log_pred = np.log10(pred)
log_meas = np.log10(meas)
obj = np.sum((log_pred - log_meas) ** 2)
return obj
@staticmethod
def slsqp_optimizer(objective, x_guess):
optimizer_kwargs = {"method": 'SLSQP',
"bounds": [(1e-1, 1e1) * len(x_guess)],
"constraints": (),
"options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}}
res = minimize(objective, x_guess, **optimizer_kwargs)
est_parameters = res.x
return est_parameters
def get_exp_csv_filename(self) -> str: def get_exp_csv_filename(self) -> str:
return self._exp_csv_filename return self._exp_csv_filename
@@ -76,6 +136,7 @@ class REEPS:
def set_exp_csv_filename(self, exp_csv_filename): def set_exp_csv_filename(self, exp_csv_filename):
self._exp_csv_filename = exp_csv_filename self._exp_csv_filename = exp_csv_filename
self._exp_df = pd.read_csv(self._exp_csv_filename) self._exp_df = pd.read_csv(self._exp_csv_filename)
self.update_predicted_dict()
return None return None
def get_phases(self) -> list: def get_phases(self) -> list:
@@ -86,8 +147,10 @@ class REEPS:
Also runs set_in_mole to set initial moles to 1 g/L""" Also runs set_in_mole to set initial moles to 1 g/L"""
self._phases_xml_filename = phases_xml_filename self._phases_xml_filename = phases_xml_filename
self._phase_names = phase_names self._phase_names = phase_names
shutil.copyfile(phases_xml_filename, self._temp_xml_file_path)
self._phases = ct.import_phases(phases_xml_filename, phase_names) self._phases = ct.import_phases(phases_xml_filename, phase_names)
self.set_in_moles(feed_vol=1) self.set_in_moles(feed_vol=1)
self.update_predicted_dict()
return None return None
def get_opt_dict(self) -> dict: def get_opt_dict(self) -> dict:
@@ -232,35 +295,72 @@ class REEPS:
] ]
in_moles_data.append(species_moles) in_moles_data.append(species_moles)
self._in_moles = pd.DataFrame(in_moles_data, columns=mixed.species_names) self._in_moles = pd.DataFrame(in_moles_data, columns=mixed.species_names)
self.update_predicted_dict()
return None return None
def get_in_moles(self) -> pd.DataFrame: def get_in_moles(self) -> pd.DataFrame:
return self._in_moles return self._in_moles
def objective(self, x): def set_objective_function(self, objective_function):
"""Log mean squared error between measured and predicted data """Set objective function. see class docstring for instructions"""
:param x: (list) thermo properties varied to minimize LMSE""" if not callable(objective_function) \
temp_xml_filename = self._temp_xml_filename and objective_function not in self._built_in_obj_list:
raise Exception(
"objective_function must be a function "
"or in this strings list: {0}".format(self._built_in_obj_list))
if callable(objective_function):
if len(signature(objective_function).parameters) < 2:
raise Exception(
"objective_function must be a function "
"with at least 3 arguments:"
" f(predicted_dict, experimental_df,**kwargs)")
if objective_function == 'Log-MSE':
objective_function = self.log_mean_squared_error
self._objective_function = objective_function
return None
def get_objective_function(self):
return self._objective_function
def set_optimizer(self, optimizer):
if not callable(optimizer) \
and optimizer not in self._built_in_opt_list:
raise Exception(
"optimizer must be a function "
"or in this strings list: {0}".format(self._built_in_opt_list))
if callable(optimizer):
if len(signature(optimizer).parameters) < 2:
raise Exception(
"optimizer must be a function "
"with at least 2 arguments: "
"f(objective_func,x_guess,**kwargs)")
if optimizer == 'SLSQP':
optimizer = self.slsqp_optimizer
self._optimizer = optimizer
return None
def get_optimizer(self):
return self._optimizer
def update_predicted_dict(self, phases_xml_filename=None):
if phases_xml_filename is None:
phases_xml_filename = self._phases_xml_filename
phase_names = self._phase_names phase_names = self._phase_names
aq_ind = self._aq_ind aq_ind = self._aq_ind
org_ind = self._org_ind org_ind = self._org_ind
complex_name = self._complex_name complex_name = self._complex_name
extractant_name = self._extractant_name
rare_earth_ion_name = self._rare_earth_ion_name rare_earth_ion_name = self._rare_earth_ion_name
in_moles = self._in_moles in_moles = self._in_moles
exp_df = self._exp_df
x = np.array(x) phases_copy = ct.import_phases(phases_xml_filename, phase_names)
opt_dict = copy.deepcopy(self._opt_dict)
i = 0
for species_name in opt_dict.keys():
for thermo_prop in opt_dict[species_name].keys():
opt_dict[species_name][thermo_prop] *= x[i]
i += 1
self.update_xml(opt_dict, temp_xml_filename)
phases_copy = ct.import_phases(temp_xml_filename, phase_names)
mix = ct.Mixture(phases_copy) mix = ct.Mixture(phases_copy)
pred = [] predicted_dict = {"re_aq": [],
"re_org": [],
"h": [],
"z": []
}
for row in in_moles.values: for row in in_moles.values:
mix.species_moles = row mix.species_moles = row
mix.equilibrate('TP', log_level=0) mix.equilibrate('TP', log_level=0)
@@ -268,17 +368,86 @@ class REEPS:
org_ind, complex_name)] org_ind, complex_name)]
re_aq = mix.species_moles[mix.species_index( re_aq = mix.species_moles[mix.species_index(
aq_ind, rare_earth_ion_name)] aq_ind, rare_earth_ion_name)]
pred.append(np.log10(re_org / re_aq)) hydrogen_ions = mix.species_moles[mix.species_index(aq_ind, 'H+')]
pred = np.array(pred) extractant = mix.species_moles[mix.species_index(
meas = np.log10(exp_df['D(m)'].values) org_ind, extractant_name)]
obj = np.sum((pred - meas) ** 2) predicted_dict['re_aq'].append(re_aq)
return obj predicted_dict['re_org'].append(re_org)
predicted_dict['h'].append(hydrogen_ions)
predicted_dict['z'].append(extractant)
predicted_dict['re_aq'] = np.array(predicted_dict['re_aq'])
predicted_dict['re_org'] = np.array(predicted_dict['re_org'])
predicted_dict['h'] = np.array(predicted_dict['h'])
predicted_dict['z'] = np.array(predicted_dict['z'])
def fit(self, kwargs) -> float: self._predicted_dict = predicted_dict
"""Fits experimental to modeled data by estimating complex reference enthalpy return None
Returns estimated complex enthalpy in J/mol
:param kwargs: (dict) parameters and options for scipy.minimize def get_predicted_dict(self):
return self._predicted_dict
def _internal_objective(self, x, kwargs=None):
"""default Log mean squared error between measured and predicted data
:param x: (list) thermo properties varied to minimize LMSE
:param kwargs: (list) arguments for objective_function
""" """
temp_xml_file_path = self._temp_xml_file_path
exp_df = self._exp_df
objective_function = self._objective_function
opt_dict = copy.deepcopy(self._opt_dict)
i = 0
for species_name in opt_dict.keys():
for _ in opt_dict[species_name].keys():
i += 1
x = np.array(x)
if i == len(x.shape):
xs = np.array([x])
vectorized_x = False
else:
vectorized_x = True
xs = x
objective_values = []
for x in xs:
i = 0
for species_name in opt_dict.keys():
for thermo_prop in opt_dict[species_name].keys():
opt_dict[species_name][thermo_prop] *= x[i]
i += 1
self.update_xml(opt_dict, temp_xml_file_path)
self.update_predicted_dict(temp_xml_file_path)
predicted_dict = self.get_predicted_dict()
if kwargs is None:
# noinspection PyCallingNonCallable
obj = objective_function(predicted_dict, exp_df)
else:
# noinspection PyCallingNonCallable
obj = objective_function(predicted_dict, exp_df, **kwargs)
objective_values.append(obj)
if vectorized_x:
objective_values = np.array(objective_values)
else:
objective_values = objective_values[0]
return objective_values
def fit(self, objective_function=None, optimizer=None, objective_kwargs=None, optimizer_kwargs=None) -> dict:
"""Fits experimental to modeled data by minimizing objective function
Returns estimated complex enthalpy in J/mol
:param objective_function: (function) function to compute objective
:param optimizer: (function) function to perform optimization
:param optimizer_kwargs: (dict) arguments for optimizer
:param objective_kwargs: (dict) arguments for objective function
"""
if objective_function is not None:
self.set_objective_function(objective_function)
if optimizer is not None:
self.set_optimizer(optimizer)
def objective(x):
return self._internal_objective(x, objective_kwargs)
optimizer = self._optimizer
opt_dict = copy.deepcopy(self._opt_dict) opt_dict = copy.deepcopy(self._opt_dict)
# x_guess = [] # x_guess = []
i = 0 i = 0
@@ -287,12 +456,20 @@ class REEPS:
# x_guess.append(opt_dict[species_name][thermo_prop]) # x_guess.append(opt_dict[species_name][thermo_prop])
i += 1 i += 1
x_guess = np.ones(i) x_guess = np.ones(i)
res = minimize(self.objective, x_guess, **kwargs)
if optimizer_kwargs is None:
# noinspection PyCallingNonCallable
est_parameters = optimizer(objective, x_guess)
else:
# noinspection PyCallingNonCallable
est_parameters = optimizer(objective, x_guess, **optimizer_kwargs)
i = 0 i = 0
for species_name in opt_dict.keys(): for species_name in opt_dict.keys():
for thermo_prop in opt_dict[species_name].keys(): for thermo_prop in opt_dict[species_name].keys():
opt_dict[species_name][thermo_prop] *= res.x[i] opt_dict[species_name][thermo_prop] *= est_parameters[i]
i += 1 i += 1
self.update_predicted_dict()
return opt_dict return opt_dict
def update_xml(self, def update_xml(self,
@@ -323,8 +500,11 @@ class REEPS:
now.year)) now.year))
tree.write(phases_xml_filename) tree.write(phases_xml_filename)
if phases_xml_filename == self._phases_xml_filename:
self.set_phases(self._phases_xml_filename, self._phase_names)
return None
def parity_plot(self): def parity_plot(self, species='re_aq'):
"""Parity plot between measured and predicted rare earth composition""" """Parity plot between measured and predicted rare earth composition"""
phases_copy = self._phases.copy() phases_copy = self._phases.copy()
mix = ct.Mixture(phases_copy) mix = ct.Mixture(phases_copy)
@@ -345,8 +525,31 @@ class REEPS:
max_data = np.max([pred, meas]) max_data = np.max([pred, meas])
min_max_data = np.array([min_data, max_data]) min_max_data = np.array([min_data, max_data])
fig, ax = plt.subplots() fig, ax = plt.subplots()
sns.scatterplot(meas, pred, color="r") sns.scatterplot(meas, pred, color="r",
sns.lineplot(min_max_data, min_max_data, color="b") label="Rare earth equilibrium concentration (mol/L)")
ax.set(xlabel='Measured X equilibrium', ylabel='Predicted X equilibrium') sns.lineplot(min_max_data, min_max_data, color="b", label="")
ax.set(xlabel='Measured', ylabel='Predicted')
plt.show() plt.show()
return None return None
def r_squared(self):
"""r-squared value comparing measured and predicted rare earth composition"""
phases_copy = self._phases.copy()
mix = ct.Mixture(phases_copy)
aq_ind = self._aq_ind
exp_df = self._exp_df
in_moles = self._in_moles
rare_earth_ion_name = self._rare_earth_ion_name
pred = []
for row in in_moles.values:
mix.species_moles = row
mix.equilibrate('TP', log_level=0)
re_aq = mix.species_moles[mix.species_index(
aq_ind, rare_earth_ion_name)]
pred.append(re_aq)
predicted_y = np.array(pred)
actual_y = exp_df['REeq(m)'].values
num = sum((actual_y - predicted_y) ** 2)
den = sum((actual_y - np.mean(actual_y)) ** 2)
r_2 = (1 - num / den)
return r_2

45
tests/test_reeps.py
View File

@@ -1,22 +1,53 @@
import json import json
import numpy as np
import pyswarms as ps
import sys import sys
sys.path.append('../') sys.path.append('../')
from reeps import REEPS from reeps import REEPS
with open('one_ree_settings.txt') as file: with open('one_ree_settings.txt') as file:
testing_params = json.load(file) testing_params = json.load(file)
beaker = REEPS(**testing_params) beaker = REEPS(**testing_params)
# def new_obj(predicted_dict, meas_df, epsilon):
# meas_cols = list(meas_df)
# pred_keys = list(predicted_dict.keys())
# meas = meas_df[meas_cols[2]]
# pred = (predicted_dict['re_org'] + epsilon) / (predicted_dict['re_aq'] + epsilon)
# log_pred = np.log10(pred)
# log_meas = np.log10(meas)
# obj = np.sum((log_pred - log_meas) ** 2)
# return obj
# #
# #
# # def new_obj(ping):
# # print(ping)
# beaker.set_objective_function(new_obj)
# objective_kwargs = {"epsilon": 1e-14}
# beaker.set
# noinspection PyUnusedLocal
def optimizer(func, x_guess):
lb = np.array([1e-1])
ub = np.array([1e1])
bounds = (lb, ub)
options = {'c1': 1e-3, 'c2': 1e-3, 'w': 0.9}
mini_optimizer = ps.single.global_best.GlobalBestPSO(n_particles=100, dimensions=1,
options=options, bounds=bounds)
f_opt, x_opt = mini_optimizer.optimize(func, iters=100)
return x_opt
minimizer_kwargs = {"method": 'SLSQP', minimizer_kwargs = {"method": 'SLSQP',
"bounds": [(1e-1, 1e1)], "bounds": [(1e-1, 1e1)],
"constraints": (), "constraints": (),
"options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}} "options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}}
est_enthalpy = beaker.fit(minimizer_kwargs) # est_enthalpy = beaker.fit(optimizer=optimizer)
est_enthalpy = beaker.fit()
print(est_enthalpy) print(est_enthalpy)
# info_dict = {"Nd(H(A)2)3(org)": {"h0": est_enthalpy}}
# # beaker.update_xml(est_enthalpy)
beaker.update_xml(est_enthalpy) # beaker.parity_plot()
beaker.parity_plot() # print(beaker.r_squared())